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En México, la caña de azúcar (Saccharum officinarum L.) es uno de los principales cultivos que tiene una gran relevancia en el desarrollo agrícola y rural. Sin embargo, el esquema utilizado para su producción representa una fuerte problemática sobre la degradación del suelo, principalmente por el uso de productos químicos. En este sentido, el uso de biofertilizantes y diversas prácticas orgánicas representan fuertes alternativas para la mejora de la fertilidad del suelo. Por lo anterior, el presente trabajo tuvo como objetivo evaluar la efectividad de la aplicación de fertilizantes sintéticos y bioles en el crecimiento y producción de biomasa de caña de azúcar (S. officinarum) en La Candelaria, Tierra Blanca, Veracruz. Se evaluaron 6 tratamientos: Tratamiento 1, fertilización tradicional 20-10-20 ha-1 de N-P-K al momento de la siembra. Tratamiento 2, fertilización tradicional 20-10-20 ha-1 de N-P-K a 30 días después de la siembra. Tratamiento 3 fertilización tradicional al 50% 20-10-20 de NPK al momento de la siembra. Tratamiento 4 fertilización tradicional + biol a 30 días después de la siembra. Tratamiento 5 biol a 30 días después de la siembra y tratamiento 6 testigo sin aplicación. Los resultados muestran que no hay diferencias significativas entre las dosis de fertilización. Por lo tanto, el uso del biol puede ser una alternativa fertilizante para los productores de caña de azúcar y puede contribuir a la transición agroecológica del cultivo.

Heliconias belong to the group of tropical flowers, which generate economic benefits in the agricultural sector with their agronomic peculiarities. Besides providing the social actors involved with benefits aimed at sustainability in different tropical areas. Therefore, the objective of this study was to compare the physiological and agronomic characteristics of three commercial cultivars of Heliconias in three locations of a community in the municipality of Fortín, in the state of Veracruz, Mexico. Three sites were identified based on the type of agriculture: agroforestry system with Heliconias (site 1), agroforestry system - monoculture, with Heliconias (site 2) and monoculture (only Heliconias, site 3). A randomised complete block design with a split-plot arrangement (A x B) and six replications was used. Factors were: A) In situ cultivation sites, corresponding to three plots (sites) in the municipality of Coapichapan, Mexico, and B) Heliconias cultivars (Heliconia psittacorum, Heliconia wagneriana and H. bihai). The variables evaluated were tillers traits and inflorescence traits. The results show that in an agroforestry system with Heliconia, Heliconia bihai showed better performance in eight variables except for inflorescence number and bract number. In an agroforestry and monoculture system with Heliconia, Heliconia bihai shows better results in leaf length and inflorescence length. In an agroforestry system and monoculture with Heliconia, Heliconia wagneriana performed better in bract number, pseudostem width and leaf length. In monoculture, Heliconia bihai responded best to inflorescence length. Due to the tropical nature of this ornamental, the best growing conditions for Heliconia are in agroforestry systems. Due to its physiological characteristics, Heliconia bihai shows its agronomic potential in this type of agriculture even under monoculture conditions. Therefore, the conditions offered by an agroforestry system have a positive effect on the cultivation of Heliconia.

Soil Moisture (SM) is a key geophysical variable that enables a better understanding of the Earth’s hydrological processes. Missions like Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) have been the primary sources of SM estimations for years. The NASA-ISRO Synthetic Aperture Radar (NISAR) mission, planned to launch in 2024, will bring L-band and S-band SAR measurements that will be used for SM estimation. Consequently, investigations to link SMAP with NISAR to produce accurate SM retrievals at improved spatial resolutions will be a focus to many research initiatives. This investigation aims to set the basis to use polarimetric Global Navigation Satellite System-Reflectometry (GNSS-R) products to enhance the temporal resolution of NISAR’s L-band SAR data through its typical 12-day period. As a proof of concept, we model SMAP radar backscatter measurements from SMAP-Reflectometry (SMAP-R) measurements, using the 3 months of data collected by the SMAP radar while it was operational. The model is based on the sensitivity of polarimetric GNSS-R to roughness, vegetation, and SM, as well as on the complementary sensitivity existing between forward and backward scatter. Different regression models are implemented using single-, dual-, and full-polarization GNSS-R measurements synthetized from SMAP-R data. This study highlights the importance of the new constellations of polarimetric GNSS-R being built and shows how those frequent measurements can serve L-band SAR missions to improve their time resolution.

The Soil Moisture Active–Passive (SMAP) mission has greatly contributed to the use of remote sensing technologies for monitoring the Earth’s land surface and estimating geophysical parameters that influence the climate system. Since the SMAP mission switched its radar receiver to allow the reception of Global Positioning System (GPS) signals, Global Navigation Satellite System Reflectometry (GNSS-R) configuration has been enabled, providing full polarimetric forward scattering measurements of the Earth’s surface, also known as SMAP Reflectometry or SMAP-R. Polarimetric GNSS-R is beneficial for sensing land surface properties, especially for more accurate estimations of soil moisture (SM) in densely vegetated areas. In this study, we explore the opportunity to enhance SMAP mission soil moisture estimates using reflected GNSS signals. We achieve this by interpolating the sparse reflectivity data with terrain information to disaggregate radiometer brightness temperatures. Our main objective is to present a novel algorithm based on Graph Signal Processing (GSP) that uses reflectometry data to enhance SMAP radiometer observations and ultimately improve SM retrievals. By implementing methods from the GSP field, we formulate the reflectivity interpolation problem as a signal reconstruction on a graph, where the weights of the edges between the nodes are chosen as a function of geophysical information. Subsequently, using the retrieved reflectivity maps, we increase the resolution of the brightness temperature data, leading to an improvement in the SM estimates. Initial findings indicate that our GSP method presents a promising alternative for analyzing sparse remote sensing observations, leveraging Earth’s surface geophysical information. This approach results in a notable improvement, with a reduced Root Mean Square Error (RMSE) of 11.8% compared to SMAP data and a reduction in unbiased RMSE (uRMSE) by 14.7% over vegetated areas.